Knowledge graph embedding based on semantic hierarchy

Abstract

In view of the current knowledge graph embedding, it mainly focuses on symmetry/opposition, inversion and combination of relationship patterns, and does not fully consider the structure of the knowledge graph. We propose a Knowledge Graph Embedding Based on Semantic Hierarchy (SHKE), which fully considers the information of knowledge graph by fusing the semantic information of the knowledge graph and the hierarchical information. The knowledge graph is mapped to a polar coordinate system, where concentric circles naturally reflect the hierarchy, and entities can be divided into modulus parts and phase parts, and then the modulus part of the polar coordinate system is mapped to the relational vector space through the relational vector, thus the modulus part takes into account the semantic information of the knowledge graph, and the phase part takes into account the hierarchical information. Experiments show that compared with other models, the proposed model improves the knowledge graph link prediction index Hits@10% by about 10% and the accuracy of the triple group classification experiment by about 10%.